By Meeyoung Cha, Diego Saez-Trumper, Jing Ma, Changwook Jung, Sung Jun Park, Geng Sun
You can see more contents and graphs through our project website: COVID-19
This repository is used to share the data we collected about COVID2019. The data files are stored in the ./Data/ directory in .csv format.
# storing and anaysis
import numpy as np
import pandas as pd
# visualization
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import seaborn as sns
from plotnine import *
import calmap
import plotly.express as px
import folium
This document provides the latest daily updates on the status of individual countries.
data = pd.read_csv("./Data/LatestDailyData.csv")
data.head()
data.info()
fig = px.bar(data[['Country/Region', 'Confirmed']].sort_values('Confirmed', ascending=False),
y="Confirmed", x="Country/Region", color='Country/Region',
log_y=True, template='ggplot2', title='Confirmed Cases')
fig.show()
This document gives the total daily confirmed, deaths, and recovery in the world
data = pd.read_csv("./Data/DailyNewNumberOfWorld.csv")
data.head()
data.info()
fig = px.bar(data, x="Date", y="Confirmed", title='Numbers of Comfirmed All Over the world')
fig.show()
fig = px.bar(data, x="Date", y="Recovered", title='Numbers of Recovered All Over the world')
fig.show()
fig = px.bar(data, x="Date", y="Deaths", title='Numbers of Deaths All Over the world')
fig.show()
This document gives the total daily diagnoses, deaths, and recovery in each counrty respctively.
data = pd.read_csv("./Data/DailyTotalNumberInEachCountry.csv")
data.head()
data.info()
fig = px.bar(data, x="Date", y="Confirmed", color='Country/Region', title='Number of Confirmed In Each Country')
fig.show()
fig = px.bar(data, x="Date", y="Recovered", color='Country/Region', title='Number of Recovered In Each Country')
fig.show()
fig = px.bar(data, x="Date", y="Deaths", color='Country/Region', title='Number of Deaths In Each Country')
fig.show()
This document gives the total accumulative confirmed, deaths, and recovery in Each country.
It is also divided into three following files:
data = pd.read_csv("./Data/TotalNumbersInEachCountry.csv")
data.head()
data.info()
fig = px.bar(data, x="Country/Region", y="Confirmed", color='Country/Region', title='Number of Confirmed In Each Country')
fig.show()
fig = px.bar(data, x="Country/Region", y="Recovered", color='Country/Region', title='Number of Recovered In Each Country')
fig.show()
fig = px.bar(data, x="Country/Region", y="Deaths", color='Country/Region', title='Number of Deaths In Each Country')
fig.show()
This document gives the total accumulative confirmed, deaths, and recovery in each province of China. This whole data have already been included in the file 4 TotalNumbersInEachCountry.csv. Still, we list it here seperately.
data = pd.read_csv("./Data/TotalNumbersInEachProvinceInChina.csv")
data.head()
data.info()
fig = px.bar(data, x="Province/State", y="Confirmed", color='Province/State', title='Number of Confirmed In Each Province of China')
fig.show()
fig = px.bar(data, x="Province/State", y="Recovered", color='Province/State', title='Number of Recovered In Each Province of China')
fig.show()
fig = px.bar(data, x="Province/State", y="Deaths", color='Province/State', title='Number of Deaths In Each Province of China')
fig.show()
You can se from the file's name. It provides the time series data of confirmed/recovered/deaths number in each country similar to File 3: DailyTotalNumberInEachCountry.csv. but we standardize it and list here seperately to give a close look at the different patterns of each country. So as File 10 : StandardizedTimeSeriesDataOfComfirmed.csv. We list the confirmed number seperately
data = pd.read_csv("./Data/StandardizedTimeSeriesData.csv")
data.head()
data.info()
plt.style.use('ggplot')
g = sns.FacetGrid(data, col="Country/Region", hue="Country/Region", sharey=False, col_wrap=5)
g = g.map(sns.lineplot, "Date", "Confirmed")
g.set_xticklabels(rotation=90)
plt.show()
plt.style.use('ggplot')
g = sns.FacetGrid(data, col="Country/Region", hue="Country/Region", sharey=False, col_wrap=5)
g = g.map(sns.lineplot, "Date", "Recovered")
g.set_xticklabels(rotation=90)
plt.show()
plt.style.use('ggplot')
g = sns.FacetGrid(data, col="Country/Region", hue="Country/Region", sharey=False, col_wrap=5)
g = g.map(sns.lineplot, "Date", "Deaths")
g.set_xticklabels(rotation=90)
plt.show()
Included in File 9 StandardizedTimeSeries.csv. Listed seperately to enhance it.
data = pd.read_csv("./Data/StandardizedTimeSeriesDataOfComfirmed.csv")
data.head()
data.info()
plt.style.use('ggplot')
g = sns.FacetGrid(data, col="Country/Region", hue="Country/Region", sharey=False, col_wrap=5)
g = g.map(sns.lineplot, "Date", "Confirmed")
g.set_xticklabels(rotation=90)
plt.show()